Public Surveillance Research Articles

Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022.

Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people's expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics. This study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics. This study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated. Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5% (n=196) of cities overlapped. In addition, Twitter-identified suicide risks were at a relatively lower level after midnight compared to a higher level in the afternoon, as well as a higher level on Sundays and Saturdays compared to weekdays. Social media platforms provide an anonymous space where people express their suicidal thoughts, ideation, and acts. Such expressions can serve as an alternative source to estimating and predicting suicide in countries without reliable suicide statistics. It can also provide real-time tracking of suicide risks, serving as an early warning for suicide. The identification of areas where suicide risks are highly concentrated is crucial for location-based mental health planning, enabling suicide prevention and intervention through social media in a spatially and temporally explicit manner.

Pathogen genomics in public health laboratories: successes, challenges, and lessons learned from California's SARS-CoV-2 Whole-Genome Sequencing Initiative, California COVIDNet.

The capacity for pathogen genomics in public health expanded rapidly during the coronavirus disease 2019 (COVID-19) pandemic, but many public health laboratories did not have the infrastructure in place to handle the vast amount of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequence data generated. The California Department of Public Health, in partnership with Theiagen Genomics, was an early adopter of cloud-based resources for bioinformatics and genomic epidemiology, resulting in the creation of a SARS-CoV-2 genomic surveillance system that combined the efforts of more than 40 sequencing laboratories across government, academia and industry to form California COVIDNet, California's SARS-CoV-2 Whole-Genome Sequencing Initiative. Open-source bioinformatics workflows, ongoing training sessions for the public health workforce, and automated data transfer to visualization tools all contributed to the success of California COVIDNet. While challenges remain for public health genomic surveillance worldwide, California COVIDNet serves as a framework for a scaled and successful bioinformatics infrastructure that has expanded beyond SARS-CoV-2 to other pathogens of public health importance.

Patient and facility characteristics of an NDM-producing Acinetobacter baumannii outbreak in California, 2020–2022

Background: Carbapenem-resistant Acinetobacter baumannii (CRAB) are bacteria that cause healthcare-associated infections and outbreaks. Most produce carbapenemases like New Delhi metallo-β-lactamase (NDM), which are more commonly found in carbapenem-resistant Enterobacterales but rarely in CRAB. In 2018, selected laboratories began participating in a public health sentinel surveillance program by routinely submitting CRAB and other antimicrobial-resistant isolates to the AR Laboratory Network for specialized testing. In May 2020, the Antimicrobial Resistance Laboratory Network detected the first NDM-CRAB case in California, triggering an investigation. Initial whole-genome sequencing of subsequent isolates indicated high relatedness. Methods: We defined confirmed cases as patients with NDM detected in CRAB isolates and probable cases as NDM detected in a screening swab from a patient epidemiologically linked to a known case(s) with specimens collected during May 2020–September 2022. We defined outbreak facilities as having (1) 1 or more newly identified cases during a point-prevalence survey in response to a known case or (2) at least 2 cases identified within 4 weeks of each other that were epidemiologically linked. We analyzed demographic and specimen characteristics, as well as healthcare exposure history using R Studio version 1.3.959 software. Results: Of 230 total patients, 176 (77%) were confirmed and 54 (23%) were probable cases; 150 (65%) were identified through colonization screening. Among 176 NDM-CRAB isolates, the most common specimen sources were respiratory (n = 29), wound (n = 28), and urine (n = 24), and 87 (49%) of 176 isolates were nonsusceptible to all antimicrobials tested. Among patients, median age was 65 years (range, 24–97), 127 (55%) were male, 37 (15%) were Hispanic or Latino, and 100 (43%) were White. We identified 37 outbreak facilities across 13 counties, including 25 acute-care hospitals (ACHs), 6 skilled nursing facilities (SNFs), 5 ventilator-equipped SNFs (vSNFs), and 1 long-term ACH. We identified 125 cases (54%) in SNFs and vSNFs and 93 cases (40%) in ACHs; among ACH patients, 43 (46%) had been SNF or vSNF residents within the prior year. No patients reported international exposure. Conclusions: The first known case of NDM-CRAB in California was detected by sentinel surveillance. In this extensive regional outbreak, most cases were identified by screening at public health and clinical laboratories. Transmission occurred across healthcare settings connected by patient sharing, underscoring the importance of communication, active surveillance, and implementation of infection prevention and control practices to mitigate spread within and between facilities. Expanding these efforts, with support and resources from public health departments, is key to detecting, characterizing, and containing future outbreaks of antimicrobial-resistant pathogens.Disclosure: None

Systematic review and meta-analysis of tuberculosis in animals in Nigeria

Animal tuberculosis (TB) is a contagious and chronic disease caused by mycobacteria belonging to theMycobacterium tuberculosis complex (MTBC) in domestic and wild animals. MTBC strains infection has been confirmed in many animal species in Nigeria, including captive wildlife, cattle, dromedary camels, goats, and pigs. Despite widespread infection and the potential impact of the disease on public health, active surveillance and control strategies are absent in Nigeria. This study aimed to conduct the first comprehensive meta-analysis to assess the distribution of tuberculosis and analyze the potential moderators of infection in animals in Nigeria. Eligible studies (sixty-one (Cadmus et al., 2014) [61] prevalence and seven (Menzies and Neill, 2000) [7] case reports) were retrieved and included in the analysis. The analyses showed an overall pooled TB prevalence of 7.0% (95% CI: 6.0–8.0) comprising of infection distributed in cattle (8.0%, 95% CI: 7.0–8.0), goats (0.47%, 95% CI: 0–1.2), sheep (0.27%, 95% CI: 0.14–0.46), camels (13.0%, 95% CI: 0–47), and wildlife (13.0%, 95% CI: 9–16) respectively. The occurrence of infection was significantly moderated by the publication periods, geographical location, sample size, and detection methods. TB prevalence was heterogeneous across several predictors, with the year of publication exhibiting a higher rate (46%) of the detected heterogeneity. These findings should provide policy-relevant information to guide the design and establishment of prevention and control measures amenable to the local situations in Nigeria.

Discovery and structure-activity relationship of Morita-Baylis-Hillman adducts as larvicides against dengue mosquito vector, Aedes aegypti (Diptera: Culicidae)

Neglected tropical diseases (NTDs) have become a significant public health problem worldwide, notably the life-threatening dengue hemorrhagic fever borne by the Aedes aegypti mosquito. Thus, mosquito vector control measures remain essential in public health vector surveillance and control to combat Aedes-borne infections. Therefore, a series of MBH adducts were synthesized and assessed towards the fourth instar mosquito larvae, Aedes aegypti, along with the preliminary structure–activity relationship (SAR). Noteworthy, this compound class might be synthetized by an efficient eco-friendly synthesismethod and a rapid route for the synthesis of commercial larvicide through a single synthetic step. The bioassays showed that this compound class is a promising larvicide to control Aedes aegypti mosquito larvae, mainly 3g, with an LC50 of 41.35 µg/mL, which was higher than evaluated positive controls. Nevertheless, it is a viable larvicidalhit candidate for further hit-to-leadproperties optimization of its biphenyl backbone scaffold with enhanced insecticidalbioactivity. Moreover, scanning electron microscopy analysis suggested a disruption of the osmoregulatory/ionoregulatory functions by the complete deterioration of the terminal exoskeleton hindgut and anal papillae. Therefore, this new study shows the larvicidal efficacy of the tested compounds against the Aedes aegypti larvae.

Application of machine learning algorithm in predicting distant metastasis of T1 gastric cancer

Distant metastasis (DM) is relatively uncommon in T1 stage gastric cancer (GC). The aim of this study was to develop and validate a predictive model for DM in stage T1 GC using machine learning (ML) algorithms. Patients with stage T1 GC from 2010 to 2017 were screened from the public Surveillance, Epidemiology and End Results (SEER) database. Meanwhile, we collected patients with stage T1 GC admitted to the Department of Gastrointestinal Surgery of the Second Affiliated Hospital of Nanchang University from 2015 to 2017. We applied seven ML algorithms: logistic regression, random forest (RF), LASSO, support vector machine, k-Nearest Neighbor, Naive Bayesian Model, Artificial Neural Network. Finally, a RF model for DM of T1 GC was developed. The AUC, sensitivity, specificity, F1-score and accuracy were used to evaluate and compare the predictive performance of the RF model with other models. Finally, we performed a prognostic analysis of patients who developed distant metastases. Independent risk factors for prognosis were analysed by univariate and multifactorial regression. K-M curves were used to express differences in survival prognosis for each variable and subvariable. A total of 2698 cases were included in the SEER dataset, 314 with DM, and 107 hospital patients were included, 14 with DM. Age, T-stage, N-stage, tumour size, grade and tumour location were independent risk factors for the development of DM in stage T1 GC. A combined analysis of seven ML algorithms in the training and test sets found that the RF prediction model had the best prediction performance (AUC: 0.941, Accuracy: 0.917, Recall: 0.841, Specificity: 0.927, F1-score: 0.877). The external validation set ROCAUC was 0.750. Meanwhile, survival prognostic analysis showed that surgery (HR = 3.620, 95% CI 2.164–6.065) and adjuvant chemotherapy (HR = 2.637, 95% CI 2.067–3.365) were independent risk factors for survival prognosis in patients with DM from stage T1 GC. Age, T-stage, N-stage, tumour size, grade and tumour location were independent risk factors for the development of DM in stage T1 GC. ML algorithms had shown that RF prediction models had the best predictive efficacy to accurately screen at-risk populations for further clinical screening for metastases. At the same time, aggressive surgery and adjuvant chemotherapy can improve the survival rate of patients with DM.

Incidence of Lyme Borreliosis in Germany: Exploring Observed Trends Over Time Using Public Surveillance Data, 2016–2020

Background: Public surveillance of Lyme borreliosis (LB) occurs in 9 out of 16 federal states of Germany and remains a critical facet of disease epidemiology and trends. We describe the incidence, time trends, seasonality, and geographic distribution of LB in Germany using publicly reported surveillance data. Methods: We obtained LB cases and incidence (2016-2020) from the online platform SurvStat@RKI 2.0, maintained by the Robert Koch Institute (RKI). Data included clinically diagnosed and laboratory-confirmed LB reported by nine out of 16 federal states of Germany where LB notification is mandatory. Results: During 2016-2020, the nine federal states reported 63,940 LB cases, of which 60,570 (94.7%) were clinically diagnosed, and 3370 (5.3%) also had laboratory confirmation, with an average of 12,789 cases annually. Incidence rates were mostly stable over time. The average annual LB incidence was 37.2/100,000 person-years and varied by spatial level, ranging from 22.9 to 64.6/100,000 person-years among nine states; from 16.8 to 85.6/100,000 person-years among 19 regions; and from 2.9 to 172.8/100,000 person-years among 158 counties. Incidence was lowest among persons 20-24 years old (16.1/100,000 person-years) and highest among those 65-69 years old (60.9/100,000 person-years). Most cases were reported between June and September, with a peak in July of every year. Conclusion: The risk of LB varied substantially at the smallest geographic unit and by age group. Our results underscore the importance of presenting LB data at the most spatially granular unit and by age to allow implementation of efficient preventive interventions and reduction strategies.

Information model of the essential goods purchase duration

The task of reducing the time for the purchase of essential goods is especially relevant in cases of shortage of free time of buyers. To do this, it is necessary to predict and estimate the time required to purchase goods. Traditional approaches based on cartographic systems do not provide estimates and forecasts, but only allow you to build a route to the right place based on an assessment of the traffic situation. For this reason, the problem of developing a more modern model is relevant, taking into account such factors as the infrastructural location of the store, user evaluation, and the workload of the store. The paper proposes an information model that includes such time costs of the buyer as the search for goods, the route to the place of sale of goods, the purchase of goods. The time spent on the purchase of goods is described using elements of queuing theory. Statistical and direct methods for assessing the workload and queues in the store are highlighted. The developed generalized model contains the parameters necessary to estimate the required time using statistical methods which include traffic forecasting based on user ratings and reviews, analysis of the infrastructure location and public video surveillance cameras, public Application Programming Interface of stores, and Internet services. Correction coefficients have been introduced to adjust the estimation of model parameters depending on the infrastructure location of the store and user ratings. A new information model has been formulated that allows taking into account the dependence of the time required to purchase emergency goods on the workload of the store, its infrastructure location, ratings and user reviews. The simulation model is developed in the AnyLogic environment. An example of using the model to estimate the average time spent on the purchase of emergency goods is demonstrated. The simulation results are consistent with the conducted experiment in which purchases of emergency goods were made in various stores in Saint Petersburg. The developed model can be used when searching for the optimal route to the place of sale of essential goods when planning the construction of stores as well as in the areas of marketing and delivery of goods.

Spatio-Temporal Clustering of Multi-Location Time Series to Model Seasonal Influenza Spread.

Although seasonal influenza disease spread is a spatio-temporal phenomenon, public surveillance systems aggregate data only spatially, and are rarely predictive. We develop a hierarchical clustering-based machine learning tool to anticipate flu spread patterns based on historical spatio-temporal flu activity, where we use historical influenza-related emergency department records as a proxy for flu prevalence. This analysis replaces conventional geographical hospital clustering with clusters based on both spatial and temporal distance between hospital flu peaks to generate a network illustrating whether flu spreads between pairs of clusters (direction) and how long that spread takes (magnitude). To overcome data sparsity, we take a model-free approach, treating hospital clusters as a fully-connected network, where arcs indicate flu transmission. We perform predictive analysis on the clusters' time series of flu ED visits to determine direction and magnitude of flu travel. Detection of recurrent spatio-temporal patterns may help policymakers and hospitals better prepare for outbreaks. We apply this tool to Ontario, Canada using a five-year historical dataset of daily flu-related ED visits, and find that in addition to expected flu spread between major cities/airport regions, we were able to illuminate previously unsuspected patterns of flu spread between non-major cities, providing new insights for public health officials. We showed that while a spatial clustering outperforms a temporal clustering in terms of the direction of the spread (81% spatial v. 71% temporal), the opposite is true in terms of the magnitude of the time lag (20% spatial v. 70% temporal).

Ethical principles for infodemiology and infoveillance studies concerning infodemic management on social media.

Big data originating from user interactions on social media play an essential role in infodemiology and infoveillance outcomes, supporting the planning and implementation of public health actions. Notably, the extrapolation of these data requires an awareness of different ethical elements. Previous studies have investigated and discussed the adoption of conventional ethical approaches in the contemporary public health digital surveillance space. However, there is a lack of specific ethical guidelines to orient infodemiology and infoveillance studies concerning infodemic on social media, making it challenging to design digital strategies to combat this phenomenon. Hence, it is necessary to explore if traditional ethical pillars can support digital purposes or whether new ones must be proposed since we are confronted with a complex online misinformation scenario. Therefore, this perspective provides an overview of the current scenario of ethics-related issues of infodemiology and infoveillance on social media for infodemic studies.

Analysis of risk factors for lymph node metastasis and prognosis study in patients with early gastric cancer: A SEER data-based study.

Lymph node status is an important factor in determining the prognosis of patients with early gastric cancer (EGC) and preoperative diagnosis of lymph node metastasis (LNM) has some limitations. This study explored the risk factors and independent prognostic factors of LNM in EGC patients and constructed a clinical prediction model to predict LNM. Clinicopathological data of EGC patients was collected from the public Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression was used to identify risk factors for LNM in EGC patients. The performance of the LNM model was evaluated by C-index, calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis (DCA) curve, and clinical impact curve (CIC) based on the results of multivariate regression to develop a nomogram. An independent data set was obtained from China for external validation. The Kaplan-Meier method and Cox regression model were used to identify potential prognostic factors for overall survival (OS) in EGC patients. A total of 3993 EGC patients were randomly allocated to a training cohort (n=2797) and a validation cohort (n=1196). An external cohort of 106 patients from the Second Hospital of Lanzhou University was used for external validation. Univariate and multivariate logistic regression showed that age, tumor size, differentiation, and examined lymph nodes count (ELNC) were independent risk factors for LNM. Nomogram for predicting LNM in EGC patients was developed and validated. The predictive model had a good discriminatory performance with a concordance index (C-index) of 0.702 (95% CI: 0.679-0.725). The calibration plots showed that the predicted LNM probabilities were the same as the actual observations in both the internal validation cohort and external validation cohort. The AUC values for the training cohort, internal validation cohort and external validation cohort were 0.702 (95% CI: 0.679-0.725), 0.709 (95% CI: 0.674-0.744) and 0.750(95% CI: 0.607-0.892), respectively, and the DCA curves and CIC showed good clinical applicability. The Cox regression model identified age, sex, race, primary site, size, pathological type, LNM, distant metastasis, and ELNC were prognostic factors for OS in EGC patients, while a year at diagnosis, grade, marital status, radiotherapy, and chemotherapy were not independent prognostic factors. In this study, we identified risk factors and independent prognostic factors for the development of LNM in EGC patients, and developed a relatively accurate model to predict the development of LNM in EGC patients.

Juridification of fandom: dealing with spectators’ expressions of ‘too much joy’ in Swedish football

ABSTRACT The essay address different forms of ‘extensive joyfulness’ among football spectators, and the Swedish legal responses to these actions, due to the actions’ classification as pitch invasion and trespassing, in legal terms. The essay presents the legal context, by focusing on external regulation of public order and surveillance as well as internal by-laws dealing with spectator security. By using two cases, from the district court, the essay reflects on the legal systems possibility and relevance in order to react on expressions of ‘too much joy’, as a legal sign of pitch invasion and the disturbance of public order. The analysi focuses on the problems of juridification when the law has to handle various mundane and ‘trivial’ social issues. The argument is that the football management has to amalgamate different forms of ‘extensive joyfulness’, in a discretionary manner, to the logics of entertainment before turning them to legal issues

Geospatial Analysis of Time to Human Immunodeficiency Virus (HIV) Diagnosis and Adult HIV Testing Coverage Highlights Areas for Intervention in the US Southeast.

In the United States (US), 44% of people with human immunodeficiency virus (PWH) live in the Southeastern census region; many PWH remain undiagnosed. Novel strategies to inform testing outreach in rural states with dispersed HIV epidemics are needed. Alabama state public health HIV testing surveillance data from 2013 to 2017 were used to estimate time from infection to HIV diagnosis using CD4 T-cell depletion modeling, mapped to county. Diagnostic HIV tests performed during 2013-2021 by commercial testing entities were used to estimate HIV tests per 100 000 adults (aged 15-65 years), mapped to client ZIP Code Tabulation Area (ZCTA). We then defined testing "cold spots": those with <10% adults tested plus either (1) within or bordering 1 of the 13 counties with HIV prevalence >400 cases per 100 000 population or (2) within a county with average time to diagnosis greater than the state average to inform testing outreach. Time to HIV diagnosis was a median of 3.7 (interquartile range [IQR], 0-9.2) years across Alabama, with a range of 0.06-12.25 years. Approximately 63% of counties (n = 42) had a longer time to diagnosis compared to national US estimates. Six hundred forty-three ZCTAs tested 17.3% (IQR, 10.3%-25.0%) of the adult population from 2013 to 2017. To prioritize areas for testing outreach, we generated maps to describe 47 areas of HIV-testing cold spots at the ZCTA level. Combining public health surveillance with commercial testing data provides a more nuanced understanding of HIV testing gaps in a state with a rural HIV epidemic and identifies areas to prioritize for testing outreach.

Bodies in Transit: Speculation and the Biopolitical Imaginary

This essay explores the Bodies in Transit archive, an artifact of mid-nineteenth-century public health administration in New York City. The ledgers, which tracked the transit of every corpse that moved through the island of Manhattan between 1859 and 1894 and categorized entrants by their cause of death, nationality, and occupation, present a unique lens through which I explore the intersections of speculation, biopolitics, and urban space. I first establish a conceptual framework of "speculation" by dissecting its etymological genealogy, the roots of which share a preoccupation with vision and sight. I note that in practice, the abstracting and rationalizing tendencies of speculation operate by envisioning, calculating, and coercing specific outcomes into realization. I apply this framework to Bodies in Transit to historicize the ways in which biopolitics, the means through which the state forms, represents, and manages populations, are indexed to speculative economic practices. I read Bodies in Transit through the framework of speculation to articulate a field of meaning that illuminates the complex material and epistemic conditions surrounding its implementation and utility. As I argue, the ledgers were a response to the acceleration of real estate speculation in Manhattan, a trend that incentivized property owners to disinter burial grounds to relocate corpses to rural areas, and thereby connected the speculative logics of real estate to those of public health, spatial order, and surveillance. By thinking across and through the layered meanings of "speculation," this essay illuminates how the state's economy of knowledge is intimately related to biopolitical practices of surveillance and abstract representations of financial value in the modern city.

Evaluating Target Audience Surveillance on Instagram in the Context of Public Relations Practices

The relationship between public relations and surveillance is a form of communication that reveals the interactions between today’s surveillance practices and companies’ sales strategies. The purpose of this article is to examine the relationships between demographic factors and individuals’ Instagram attitudes and behaviors. The article also looks at the relationships among levels of corporate trust, the sharing of private information on Instagram, individuals’ motivation to log in, the time spent daily on Instagram, and the number of accounts followed. Two-way ANOVA and post hoc tests have been used to examine with respect to demographics the differences between levels of institutional trust and levels at which relatively private information is sent. The relationships among level of institutional trust, level at which relatively private information is posted, motivation to log on, time spent daily on Instagram, and number of accounts followed have been examined using discriminant analysis. Individuals’ beliefs about protecting personal information and preventing unauthorized access have been determined to differ according to age group. Likewise, the level at which individuals post relatively private information on Instagram varies by age group. Also, a significant difference has been found for individuals’ level of institutional trust with their education and income levels. A significant relationship has been found for individuals’ institutional trust levels with their individual posting levels, motivation to log on, and time spent daily on Instagram. As a result, this study contributes to institutions that carry out public relations activities on Instagram and their target audiences with regard to recognizing their trust levels toward Instagram and institutions and their behaviors on Instagram.

Reclassification of the Etiology of Infant Mortality With Whole-Genome Sequencing

Understanding the causes of infant mortality shapes public health, surveillance, and research investments. However, the association of single-locus (mendelian) genetic diseases with infant mortality is poorly understood. To determine the association of genetic diseases with infant mortality. This cohort study was conducted at a large pediatric hospital system in San Diego County (California) and included 546 infants (112 infant deaths [20.5%] and 434 infants [79.5%] with acute illness who survived; age, 0 to 1 year) who underwent diagnostic whole-genome sequencing (WGS) between January 2015 and December 2020. Data analysis was conducted between 2015 and 2022. Infants underwent WGS either premortem or postmortem with semiautomated phenotyping and diagnostic interpretation. Proportion of infant deaths associated with single-locus genetic diseases. Among 112 infant deaths (54 girls [48.2%]; 8 [7.1%] African American or Black, 1 [0.9%] American Indian or Alaska Native, 8 [7.1%] Asian, 48 [42.9%] Hispanic, 1 [0.9%] Native Hawaiian or Pacific Islander, and 34 [30.4%] White infants) in San Diego County between 2015 and 2020, single-locus genetic diseases were the most common identifiable cause of infant mortality, with 47 genetic diseases identified in 46 infants (41%). Thirty-nine (83%) of these diseases had been previously reported to be associated with childhood mortality. Twenty-eight death certificates (62%) for 45 of the 46 infants did not mention a genetic etiology. Treatments that can improve outcomes were available for 14 (30%) of the genetic diseases. In 5 of 7 infants in whom genetic diseases were identified postmortem, death might have been avoided had rapid, diagnostic WGS been performed at time of symptom onset or regional intensive care unit admission. In this cohort study of 112 infant deaths, the association of genetic diseases with infant mortality was higher than previously recognized. Strategies to increase neonatal diagnosis of genetic diseases and immediately implement treatment may decrease infant mortality. Additional study is required to explore the generalizability of these findings and measure reduction in infant mortality.

Establishment of quantitative and recovery method for detection of dengue virus in wastewater with noncognate spike control

Wastewater-based epidemiology (WBE) represents an efficient approach for public pathogen surveillance as it provides early warning of disease outbreaks; however, it has not yet been applied to dengue virus (DENV), which might cause endemics via mosquito spread. In this study, a working platform was established to provide direct virus recovery and qPCR quantification from wastewater samples that were artificially loaded with target DENV serotypes I to IV and noncognate spike control viral particles. The results showed qPCR efficiencies of 91.2 %, 94.8 %, 92.6 % and 88.7 % for DENV I, II, III, and IV, respectively, and a broad working range over 6 orders of magnitude using the preferred primer sets. Next, the results revealed that the ultrafiltration method was superior to the skimmed milk flocculation method for recovering either DENV or control viral particles from wastewater. Finally, DENV-2 was loaded simultaneously with the noncognate spike control and could be recovered at comparable levels either in PBS or in wastewater, indicating the applicability of noncognate spike control particles to reflect the efficiency of experimental steps. In conclusion, our data suggest that DENV particles in wastewater could be recovered and quantitatively detected in absolute amounts, indicating the feasibility of DENV surveillance using the WBE approach.

Locate, Tell, and Guide: Enabling Public Cameras to Navigate the Public

Indoor navigation is essential to a wide spectrum of applications in the era of mobile computing. Existing vision-based technologies suffer from both start-up costs and the absence of semantic information for navigation. We observe an opportunity to leverage pervasively deployed surveillance cameras to deal with the above drawbacks and revisit the problem of indoor navigation with a fresh perspective. In this paper, we propose <i>iSAT</i> , a system that enables public surveillance cameras, as indoor navigating satellites, to locate users on the floorplan, tell users with semantic information about the surrounding environment, and guide users with navigation instructions. However, enabling public cameras to navigate is non-trivial due to 3 factors: absence of real scale, disparity of camera perspective, and lack of semantic information. To overcome these challenges, <i>iSAT</i> leverages POI-assisted framework and adopts a novel coordinate transformation algorithm to associate public and mobile cameras, and further attaches semantic information to user location. Extensive experiments in 4 different scenarios show that <i>iSAT</i> achieves a localization accuracy of 0.48m and a navigation success rate of 90.5 percent, outperforming the state-of-th-art systems by <inline-formula><tex-math notation="LaTeX">$&gt; 30\%$</tex-math></inline-formula> . Benefiting from our solution, all areas with public cameras can upgrade to smart spaces with visual navigation services.

Implementing Health Information System in King Hamad University Hospital Benefits and Challenges

As a university hospital, it prides itself on using the latest techniques, medical and diagnostic curriculum, as well as up-to-the-minute medical equipment and fully equipped facilities. It has over 1700 employees and accommodates more than 311 beds and 9 operating theatres, a number of internal wards plus an intensive care unit with 12 beds. Healthcare organizations now believe in improved efficiency, reduced costs, improved patient care and quality of services, and safety when they are preparing to apply new information and communication technology (ICT) based applications. Health IT systems allow the gathering of data for use for quality management, outcome reporting, and public health disease surveillance and reporting. However, development is needed with all health IT, particularly concerning design, implementation, and integration between platforms within the surroundings of work.

Climate change and communicable diseases in the Gulf Cooperation Council (GCC) countries

A review of the extant literature reveals the extent to which the spread of communicable diseases will be significantly impacted by climate change. Specific research into how this will likely be observed in the countries of the Gulf Cooperation Council (GCC) is, however, greatly lacking. This report summarises the unique public health challenges faced by the GCC countries in the coming century, and outlines the need for greater investment in public health research and disease surveillance to better forecast the imminent epidemiological landscape. Significant data gaps currently exist regarding vector occurrence, spatial climate measures, and communicable disease case counts in the GCC — presenting an immediate research priority for the region. We outline policy work necessary to strengthen public health interventions, and to facilitate evidence-driven mitigation strategies. Such research will require a transdisciplinary approach, utilising existing cross-border public health initiatives, to ensure that such investigations are well-targeted and effectively communicated.